Chapter 5 Community composition

5.1 Filter data

load("data/data.Rdata")

Filter samples with high host data

sample_metadata <- sample_metadata %>%
  filter(!sample %in% c("EHI02721", "EHI02712", "EHI02700", "EHI02720", "EHI02749", "EHI02719", "EHI02729", "EHI02715", "EHI02722"))

genome_counts_filt <- genome_counts %>%
  select(one_of(c("genome",sample_metadata$sample)))%>%
  filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
  select_if(~!all(. == 0))
genome_counts <- genome_counts_filt
genome_metadata <- genome_metadata %>% 
  semi_join(., genome_counts_filt, by = "genome") %>% 
  arrange(match(genome,genome_counts_filt$genome))

genome_tree <- keep.tip(genome_tree, tip=genome_metadata$genome) # keep only MAG tips

#load("data/genome_gifts.Rdata")

Make a phyloseq object

phylo_samples <- sample_metadata %>% 
  column_to_rownames("sample") %>% 
  sample_data() #convert to phyloseq sample_data object
phylo_genome <- genome_counts_filt %>% 
  column_to_rownames("genome") %>% 
  otu_table(., taxa_are_rows = TRUE)
phylo_taxonomy <- genome_metadata %>%
  column_to_rownames("genome") %>% 
  as.matrix() %>% 
  tax_table() #convert to phyloseq tax_table object
phylo_tree <- phy_tree(genome_tree) 

physeq_genome <- phyloseq(phylo_genome, phylo_taxonomy, phylo_samples,phylo_tree)
physeq_genome_clr <- microbiome::transform(physeq_genome, 'clr')
save(sample_metadata, 
     genome_metadata, 
     read_counts, 
     genome_counts, 
     genome_counts_filt, 
     genome_tree,
     physeq_genome,
     physeq_genome_clr,
     genome_gifts_raw, 
#     genome_gifts,
     phylum_colors,
     diet_colors,
     file = "data/data_host_filtered.Rdata")

5.2 Load data

load("data/data_host_filtered.Rdata")

5.3 Taxonomy overview

5.3.1 Stacked barplot

genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
  filter(diet!="Post_grass") %>% 
  filter(count > 0) %>% #filter 0 counts
  ggplot(., aes(x=sample,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors) +
  facet_grid(~factor(diet, labels=c("Pre_grass" = "Captive-born","Wild" = "Wild-born")), scale="free", space = "free")+
 #   facet_nested(. ~ region+diet,  scales="free") + #facet per day and treatment
    guides(fill = guide_legend(ncol = 1)) +
    theme(
          axis.title.x = element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          strip.background = element_rect(fill = "white"),
    strip.text = element_text(size = 12, lineheight = 0.6),
    strip.placement = "outside",
    axis.text.x = element_blank(), axis.ticks.x = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black")) +
   labs(fill="Phylum",y = "Relative abundance",x="Samples")

Number of bacteria phyla

[1] 14

Bacteria phyla in wild individuals

[1] 14

Bacteria phyla captive animals

[1] 14

Bacteria phyla before grass is included in the diet

[1] 14

Bacteria phyla after grass is included in the diet

[1] 14

Number of Archaea phyla

[1] 1

Archaea phyla in wild individuals

[1] 0

Archaea phyla before grass is included in the diet

[1] "p__Methanobacteriota"

Archaea phyla after grass is included in the diet

[1] "p__Methanobacteriota"

5.3.2 Genus and species annotation

Number of MAGs without species-level annotation

# A tibble: 1 × 1
  Mag_nospecies
          <int>
1           749
# A tibble: 14 × 4
   phylum               count_nospecies count_total percentage
   <chr>                          <int>       <int>      <dbl>
 1 p__Actinomycetota                 15          15      100  
 2 p__Bacillota                      52          53       98.1
 3 p__Bacillota_A                   516         526       98.1
 4 p__Bacillota_B                     2           2      100  
 5 p__Bacillota_C                     6           9       66.7
 6 p__Bacteroidota                   43         127       33.9
 7 p__Campylobacterota                1           1      100  
 8 p__Cyanobacteriota                 6           7       85.7
 9 p__Desulfobacterota               12          12      100  
10 p__Patescibacteria                13          13      100  
11 p__Pseudomonadota                 32          39       82.1
12 p__Spirochaetota                   2           2      100  
13 p__Synergistota                   18          18      100  
14 p__Verrucomicrobiota              31          35       88.6

Percentage of MAGs without species-level annotation

[1] 87.09302

Number of MAGs without genera-level annotation

79

5.3.3 Phylum relative abundances

phylum_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == sample)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum,region, diet) %>%
  summarise(relabun=sum(count))
phylum_arrange <- phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    pull()

phylum_summary %>%
    filter(phylum %in% phylum_arrange) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange))) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
        scale_color_manual(values=phylum_colors[rev(phylum_arrange)]) +
        geom_jitter(alpha=0.5) + 
        theme_minimal() + 
        theme(legend.position="none") +
        labs(y="Phylum",x="Relative abundance")

5.3.3.1 Origin: Wild vs Captive

phylum_summary %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Captive_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Captive_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T)) %>%
    mutate(total=str_c(round(total_mean,3),"±",round(total_sd,3)),
           Wild=str_c(round(Wild_mean,3),"±",round(Wild_sd,3)),
           Captive=str_c(round(Captive_mean,3),"±",round(Captive_sd,3))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total,Wild,Captive)
# A tibble: 15 × 4
   phylum               total         Wild          Captive      
   <chr>                <chr>         <chr>         <chr>        
 1 p__Bacillota_A       57.243±12.618 48.636±13.52  59.846±10.725
 2 p__Bacteroidota      25.787±10.113 26.889±11.805 24.986±10.083
 3 p__Bacillota         4.118±7.202   5.066±11.152  4.949±5.297  
 4 p__Spirochaetota     3.911±10.011  11.731±14.792 0.001±0.001  
 5 p__Verrucomicrobiota 2.264±5.722   1.472±0.715   1.449±1.295  
 6 p__Pseudomonadota    1.626±3.328   0.511±0.397   3.082±5.382  
 7 p__Patescibacteria   0.987±1.542   0.212±0.253   2.177±2.223  
 8 p__Synergistota      0.96±0.852    1.865±0.83    0.45±0.39    
 9 p__Bacillota_C       0.844±0.795   1.696±0.674   0.386±0.36   
10 p__Actinomycetota    0.78±0.985    0.427±0.459   1.217±1.147  
11 p__Cyanobacteriota   0.708±2.081   0.277±0.614   0.916±3.024  
12 p__Desulfobacterota  0.586±0.396   0.991±0.315   0.41±0.256   
13 p__Bacillota_B       0.104±0.145   0.228±0.203   0.042±0.024  
14 p__Methanobacteriota 0.082±0.217   0±0           0.09±0.228   
15 p__Campylobacterota  0±0           0±0           0±0          

5.3.3.2 Origin and diet

phylum_summary %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Pre_grass_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Pre_grass_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Post_grass_mean=mean(relabun[diet=="Post_grass"]*100, na.rm=T),
              Post_grass_sd=sd(relabun[diet=="Post_grass"]*100, na.rm=T))  %>%
    mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Pre_grass=str_c(round(Pre_grass_mean,6),"±",round(Pre_grass_sd,6)),
           Post_grass=str_c(round(Post_grass_mean,2),"±",round(Post_grass_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total,Wild,Pre_grass,Post_grass)
# A tibble: 15 × 5
   phylum               total       Wild        Pre_grass           Post_grass
   <chr>                <chr>       <chr>       <chr>               <chr>     
 1 p__Bacillota_A       57.24±12.62 48.64±13.52 59.845932±10.725422 63.25±9.01
 2 p__Bacteroidota      25.79±10.11 26.89±11.81 24.986055±10.082621 25.49±9.07
 3 p__Bacillota         4.12±7.2    5.07±11.15  4.94862±5.296738    2.34±2.73 
 4 p__Spirochaetota     3.91±10.01  11.73±14.79 0.00063±0.000727    0±0       
 5 p__Verrucomicrobiota 2.26±5.72   1.47±0.71   1.448674±1.294628   3.87±9.89 
 6 p__Pseudomonadota    1.63±3.33   0.51±0.4    3.082488±5.382343   1.29±1.52 
 7 p__Patescibacteria   0.99±1.54   0.21±0.25   2.176993±2.223404   0.57±0.41 
 8 p__Synergistota      0.96±0.85   1.86±0.83   0.449802±0.390105   0.57±0.35 
 9 p__Bacillota_C       0.84±0.79   1.7±0.67    0.385614±0.359883   0.45±0.49 
10 p__Actinomycetota    0.78±0.99   0.43±0.46   1.217389±1.147372   0.7±1.1   
11 p__Cyanobacteriota   0.71±2.08   0.28±0.61   0.915635±3.024426   0.93±1.99 
12 p__Desulfobacterota  0.59±0.4    0.99±0.31   0.410146±0.255958   0.36±0.25 
13 p__Bacillota_B       0.1±0.15    0.23±0.2    0.041532±0.023962   0.04±0.02 
14 p__Methanobacteriota 0.08±0.22   0±0         0.090366±0.227643   0.15±0.29 
15 p__Campylobacterota  0±0         0±0         0.000127±0.000226   0±0       
phylum_arrange <- phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    pull()

phylum_summary %>%
    left_join(genome_metadata %>% select(phylum,phylum) %>% unique(),by=join_by(phylum==phylum)) %>%
#    left_join(sample_metadata,by=join_by(sample==sample)) %>%
    filter(phylum %in% phylum_arrange[1:20]) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
        scale_color_manual(values=phylum_colors[-8]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~diet)+
        theme_minimal() + 
        labs(y="phylum", x="Relative abundance", color="Phylum")

5.4 Taxonomy boxplot

5.4.1 Family

family_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,family, diet,region) %>%
  summarise(relabun=sum(count))
family_summary$diet <- factor(family_summary$diet, levels=c("Pre_grass", "Post_grass", "Wild"))

5.4.1.1 Origin: Wild vs Captive

family_summary %>%
    group_by(family) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Cap_mean=mean(relabun[region=="Nafarroa"]*100, na.rm=T),
              Cap_sd=sd(relabun[region=="Nafarroa"]*100, na.rm=T))  %>%
    mutate(Total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Captive=str_c(round(Cap_mean,2),"±",round(Cap_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(family,Total,Wild,Captive) %>% 
    tt()
family Total Wild Captive
f__Lachnospiraceae 21.69±9.91 18.46±10.79 23.3±9.26
f__Bacteroidaceae 17.22±8.91 18.2±9.79 16.73±8.61
f__Ruminococcaceae 10.01±7.82 9.16±9.9 10.44±6.76
f__Oscillospiraceae 7.11±4.79 9.05±6.73 6.14±3.21
f__Borkfalkiaceae 4.87±7.57 1.52±3.18 6.55±8.57
f__Sphaerochaetaceae 3.91±10.01 11.73±14.79 0±0
f__CAG-508 3.24±3.94 0.45±0.37 4.63±4.19
f__Marinifilaceae 2.49±1.42 3.1±1.62 2.19±1.23
f__CAG-74 2.47±2.63 4.7±3.41 1.36±1.04
f__UBA660 2.26±3.79 0.05±0.06 3.37±4.25
f__Acutalibacteraceae 2.14±1.03 1.88±1.17 2.28±0.95
f__Rikenellaceae 2.01±1.42 2.02±1.9 2.01±1.17
f__CAG-314 1.89±4.51 0.04±0.03 2.81±5.32
f__CAG-449 1.69±6.7 5.02±11.16 0.02±0.06
f__Muribaculaceae 1.33±1.73 0.28±0.48 1.85±1.9
f__UBA932 1.26±2.12 2.09±3.28 0.84±1.06
f__Victivallaceae 1.23±0.94 1.41±0.75 1.15±1.02
f__Tannerellaceae 1.2±0.61 1.12±0.79 1.25±0.5
f__Nanosyncoccaceae 0.99±1.54 0.21±0.25 1.37±1.77
f__Akkermansiaceae 0.97±5.81 0±0 1.45±7.12
f__Selenomonadaceae 0.73±0.81 1.69±0.67 0.24±0.23
f__Gastranaerophilaceae 0.71±2.08 0.28±0.61 0.92±2.5
f__CAG-274 0.67±0.66 0.8±0.64 0.6±0.68
f__Dethiosulfovibrionaceae 0.59±0.56 1.25±0.44 0.25±0.19
f__Desulfovibrionaceae 0.59±0.4 0.99±0.31 0.38±0.25
f__Atopobiaceae 0.53±0.85 0.2±0.23 0.69±0.99
f__Burkholderiaceae_A 0.51±0.39 0.46±0.39 0.54±0.4
f__CAG-239 0.49±1.84 0.03±0.1 0.72±2.23
f__CAG-917 0.42±0.68 0±0 0.63±0.75
f__Monoglobaceae 0.41±0.38 0.23±0.39 0.49±0.35
f__Synergistaceae 0.37±0.41 0.61±0.52 0.26±0.29
f__UMGS1883 0.37±0.41 0.19±0.29 0.46±0.43
f__UBA1381 0.36±0.31 0.37±0.22 0.35±0.35
f__UBA1242 0.34±0.65 0.12±0.13 0.46±0.77
f__Anaerovoracaceae 0.23±0.36 0.65±0.35 0.02±0.03
f__Eggerthellaceae 0.23±0.21 0.23±0.24 0.22±0.19
f__CAG-272 0.22±0.28 0.03±0.02 0.32±0.3
f__Pumilibacteraceae 0.21±0.39 0.02±0.03 0.31±0.45
f__Burkholderiaceae_C 0.18±1 0±0 0.26±1.22
f__Butyricicoccaceae 0.15±0.47 0.43±0.76 0.01±0.02
f__UBA1820 0.14±0.15 0.08±0.09 0.17±0.16
f__JAAYOS01 0.13±0.18 0.05±0.07 0.17±0.2
f__Acidaminococcaceae 0.12±0.38 0±0 0.18±0.45
f__Flavobacteriaceae 0.1±0.57 0±0 0.14±0.7
f__Halomonadaceae 0.09±0.29 0±0 0.14±0.35
f__ 0.08±0.21 0.17±0.28 0.04±0.15
f__Wohlfahrtiimonadaceae 0.08±0.48 0±0 0.12±0.58
f__Methanobacteriaceae 0.08±0.22 0±0 0.12±0.26
f__UBA644 0.08±0.15 0.02±0.06 0.11±0.18
f__Pseudomonadaceae 0.07±0.41 0±0.01 0.11±0.5
f__Alteromonadaceae 0.07±0.24 0.02±0.04 0.1±0.29
f__Xanthomonadaceae 0.07±0.24 0±0 0.1±0.29
f__Erysipelotrichaceae 0.07±0.15 0±0 0.1±0.18
f__UBA7702 0.06±0.11 0.12±0.17 0.03±0.02
f__UBA1829 0.06±0.12 0.06±0.17 0.06±0.08
f__UBA1750 0.06±0.11 0.15±0.16 0.01±0.03
f__Salinicoccaceae 0.06±0.19 0±0 0.09±0.22
f__Eubacteriaceae 0.05±0.1 0.1±0.15 0.02±0.04
f__Aeromonadaceae 0.05±0.27 0±0 0.07±0.33
f__Peptococcaceae 0.04±0.06 0.1±0.07 0.01±0.01
f__Balneolaceae 0.03±0.14 0±0 0.05±0.17
f__Staphylococcaceae 0.03±0.12 0±0 0.04±0.14
f__Mycobacteriaceae 0.03±0.06 0±0 0.04±0.07
f__Anaerotignaceae 0.02±0.04 0.06±0.05 0±0.01
f__Oleiphilaceae 0.02±0.08 0±0 0.03±0.1
f__Coprobacillaceae 0.02±0.1 0±0 0.03±0.12
f__CAG-382 0.01±0.04 0±0 0.01±0.05
f__Weeksellaceae 0±0 0±0 0±0
f__Neisseriaceae 0±0 0±0 0±0
f__Streptococcaceae 0±0 0±0 0±0
f__Helicobacteraceae 0±0 0±0 0±0
f__Moraxellaceae 0±0 0±0 0±0
f__Pasteurellaceae 0±0 0±0 0±0
f__UBA2023 0±0 0±0 0±0
f__Cardiobacteriaceae 0±0 0±0 0±0

5.4.1.2 Origin and Diet

family_summary %>%
    group_by(family) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Pre_grass_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Pre_grass_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Post_grass_mean=mean(relabun[diet=="Post_grass"]*100, na.rm=T),
              Post_grass_sd=sd(relabun[diet=="Post_grass"]*100, na.rm=T))  %>%
    mutate(Total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Pre_grass=str_c(round(Pre_grass_mean,2),"±",round(Pre_grass_sd,2)),
           Post_grass=str_c(round(Post_grass_mean,2),"±",round(Post_grass_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(family,Total,Wild,Pre_grass,Post_grass) %>% 
    tt()
family Total Wild Pre_grass Post_grass
f__Lachnospiraceae 21.69±9.91 18.46±10.79 23.69±8.7 22.91±10.16
f__Bacteroidaceae 17.22±8.91 18.2±9.79 16.79±9.7 16.68±7.82
f__Ruminococcaceae 10.01±7.82 9.16±9.9 11.75±8.53 9.12±4.36
f__Oscillospiraceae 7.11±4.79 9.05±6.73 5.21±2.15 7.08±3.87
f__Borkfalkiaceae 4.87±7.57 1.52±3.18 4.6±5.72 8.49±10.62
f__Sphaerochaetaceae 3.91±10.01 11.73±14.79 0±0 0±0
f__CAG-508 3.24±3.94 0.45±0.37 5.81±4.22 3.46±3.98
f__Marinifilaceae 2.49±1.42 3.1±1.62 2.11±1.3 2.27±1.21
f__CAG-74 2.47±2.63 4.7±3.41 1.03±0.72 1.68±1.24
f__UBA660 2.26±3.79 0.05±0.06 4.57±5.23 2.17±2.7
f__Acutalibacteraceae 2.14±1.03 1.88±1.17 2.3±0.79 2.26±1.13
f__Rikenellaceae 2.01±1.42 2.02±1.9 2.14±1.45 1.88±0.83
f__CAG-314 1.89±4.51 0.04±0.03 1.54±2.53 4.08±7.01
f__CAG-449 1.69±6.7 5.02±11.16 0.03±0.08 0±0
f__Muribaculaceae 1.33±1.73 0.28±0.48 1.23±1.33 2.46±2.22
f__UBA932 1.26±2.12 2.09±3.28 1.04±1.21 0.65±0.9
f__Victivallaceae 1.23±0.94 1.41±0.75 1.38±1.25 0.91±0.71
f__Tannerellaceae 1.2±0.61 1.12±0.79 1.17±0.58 1.32±0.42
f__Nanosyncoccaceae 0.99±1.54 0.21±0.25 2.18±2.22 0.57±0.41
f__Akkermansiaceae 0.97±5.81 0±0 0±0 2.9±10.06
f__Selenomonadaceae 0.73±0.81 1.69±0.67 0.21±0.19 0.27±0.28
f__Gastranaerophilaceae 0.71±2.08 0.28±0.61 0.92±3.02 0.93±1.99
f__CAG-274 0.67±0.66 0.8±0.64 0.59±0.5 0.62±0.84
f__Dethiosulfovibrionaceae 0.59±0.56 1.25±0.44 0.19±0.15 0.32±0.21
f__Desulfovibrionaceae 0.59±0.4 0.99±0.31 0.41±0.26 0.36±0.25
f__Atopobiaceae 0.53±0.85 0.2±0.23 0.88±0.98 0.51±1
f__Burkholderiaceae_A 0.51±0.39 0.46±0.39 0.6±0.52 0.47±0.24
f__CAG-239 0.49±1.84 0.03±0.1 0.99±3.04 0.44±1
f__CAG-917 0.42±0.68 0±0 0.61±0.76 0.66±0.77
f__Monoglobaceae 0.41±0.38 0.23±0.39 0.56±0.39 0.42±0.3
f__Synergistaceae 0.37±0.41 0.61±0.52 0.26±0.38 0.25±0.17
f__UMGS1883 0.37±0.41 0.19±0.29 0.31±0.29 0.6±0.51
f__UBA1381 0.36±0.31 0.37±0.22 0.3±0.37 0.41±0.33
f__UBA1242 0.34±0.65 0.12±0.13 0.41±0.58 0.5±0.94
f__Anaerovoracaceae 0.23±0.36 0.65±0.35 0.02±0.02 0.02±0.03
f__Eggerthellaceae 0.23±0.21 0.23±0.24 0.29±0.22 0.16±0.13
f__CAG-272 0.22±0.28 0.03±0.02 0.42±0.39 0.21±0.13
f__Pumilibacteraceae 0.21±0.39 0.02±0.03 0.25±0.29 0.37±0.57
f__Burkholderiaceae_C 0.18±1 0±0 0.53±1.72 0±0.01
f__Butyricicoccaceae 0.15±0.47 0.43±0.76 0.01±0.01 0.02±0.02
f__UBA1820 0.14±0.15 0.08±0.09 0.2±0.19 0.15±0.13
f__JAAYOS01 0.13±0.18 0.05±0.07 0.21±0.25 0.14±0.15
f__Acidaminococcaceae 0.12±0.38 0±0 0.17±0.41 0.18±0.51
f__Flavobacteriaceae 0.1±0.57 0±0 0.28±0.98 0±0
f__Halomonadaceae 0.09±0.29 0±0 0.15±0.36 0.13±0.36
f__ 0.08±0.21 0.17±0.28 0.07±0.21 0.01±0.01
f__Wohlfahrtiimonadaceae 0.08±0.48 0±0 0.25±0.82 0±0
f__Methanobacteriaceae 0.08±0.22 0±0 0.09±0.23 0.15±0.29
f__UBA644 0.08±0.15 0.02±0.06 0.09±0.05 0.14±0.25
f__Pseudomonadaceae 0.07±0.41 0±0.01 0.21±0.71 0.01±0.01
f__Alteromonadaceae 0.07±0.24 0.02±0.04 0.12±0.38 0.08±0.18
f__Xanthomonadaceae 0.07±0.24 0±0 0.06±0.16 0.14±0.38
f__Erysipelotrichaceae 0.07±0.15 0±0 0.11±0.18 0.09±0.18
f__UBA7702 0.06±0.11 0.12±0.17 0.03±0.02 0.04±0.02
f__UBA1829 0.06±0.12 0.06±0.17 0.07±0.1 0.06±0.06
f__UBA1750 0.06±0.11 0.15±0.16 0.01±0.01 0.02±0.04
f__Salinicoccaceae 0.06±0.19 0±0 0.1±0.31 0.07±0.09
f__Eubacteriaceae 0.05±0.1 0.1±0.15 0.04±0.05 0±0.01
f__Aeromonadaceae 0.05±0.27 0±0 0.14±0.46 0±0
f__Peptococcaceae 0.04±0.06 0.1±0.07 0.01±0.01 0.01±0.01
f__Balneolaceae 0.03±0.14 0±0 0.02±0.08 0.07±0.23
f__Staphylococcaceae 0.03±0.12 0±0 0.08±0.2 0.01±0.02
f__Mycobacteriaceae 0.03±0.06 0±0 0.05±0.08 0.04±0.05
f__Anaerotignaceae 0.02±0.04 0.06±0.05 0±0.01 0.01±0.01
f__Oleiphilaceae 0.02±0.08 0±0 0.04±0.13 0.02±0.03
f__Coprobacillaceae 0.02±0.1 0±0 0.05±0.17 0±0
f__CAG-382 0.01±0.04 0±0 0.02±0.08 0±0.01
f__Weeksellaceae 0±0 0±0 0±0 0±0
f__Neisseriaceae 0±0 0±0 0±0 0±0
f__Streptococcaceae 0±0 0±0 0±0 0±0
f__Helicobacteraceae 0±0 0±0 0±0 0±0
f__Moraxellaceae 0±0 0±0 0±0 0±0
f__Pasteurellaceae 0±0 0±0 0±0 0±0
f__UBA2023 0±0 0±0 0±0 0±0
f__Cardiobacteriaceae 0±0 0±0 0±0 0±0
family_arrange <- family_summary %>%
    group_by(family) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(family) %>%
    pull()

family_summary %>%
    left_join(genome_metadata %>% select(family,phylum) %>% unique(),by=join_by(family==family)) %>%
    filter(family %in% family_arrange[1:20]) %>%
    mutate(family=factor(family,levels=rev(family_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=family, group=family, color=phylum)) +
        scale_color_manual(values=phylum_colors[-8]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~diet)+
        theme_minimal() + 
        labs(y="Family", x="Relative abundance", color="Phylum")

5.4.2 Genus

genus_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
  left_join(genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,phylum,genus, diet) %>%
  summarise(relabun=sum(count)) %>%
  filter(genus != "g__") %>%
  mutate(genus= sub("^g__", "", genus))
genus_summary$diet <- factor(genus_summary$diet, levels=c("Pre_grass", "Post_grass", "Wild"))

5.4.3 origin and diet

genus_summary %>%
    group_by(genus) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Pre_grass_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Pre_grass_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Post_grass_mean=mean(relabun[diet=="Post_grass"]*100, na.rm=T),
              Post_grass_sd=sd(relabun[diet=="Post_grass"]*100, na.rm=T))  %>%
    mutate(Total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Pre_grass=str_c(round(Pre_grass_mean,2),"±",round(Pre_grass_sd,2)),
           Post_grass=str_c(round(Post_grass_mean,2),"±",round(Post_grass_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(genus,Total,Wild,Pre_grass,Post_grass) %>% 
    tt()
genus Total Wild Pre_grass Post_grass
Bacteroides 11.45±7.42 10.71±8.63 12.01±7.78 11.63±6.26
Eisenbergiella 9.15±9.45 4.11±11.28 11.85±6.46 11.49±8.57
Phocaeicola 4.75±2.03 5.26±2.32 4.37±2.02 4.64±1.78
UBA9732 3.91±10.01 11.73±14.79 0±0 0±0
Coproplasma 2.59±5.68 0.67±2.29 2.05±3.56 5.05±8.59
Gallimonas 2.28±4.19 0.85±2.4 2.55±3.63 3.44±5.77
UBA7477 2.17±5.94 0.02±0.01 4.95±9.5 1.54±2.86
CAJOIG01 1.97±1.72 2.73±1.76 1.42±1.39 1.77±1.82
Alistipes 1.84±1.42 1.93±1.94 1.91±1.43 1.68±0.74
Fimimonas 1.83±4.48 0.03±0.03 1.47±2.55 4±6.97
Enterenecus 1.78±2.42 3.34±3.63 0.76±0.38 1.23±1.07
UBA1366 1.36±1.8 0.27±0.18 1.68±2.05 2.13±2
Ruminococcus_D 1.3±1.55 0.24±0.28 1.85±1.89 1.81±1.47
CAG-269 1.3±1.53 0.21±0.17 2.39±1.62 1.29±1.47
Egerieousia 1.26±2.12 2.09±3.28 1.04±1.21 0.65±0.9
Gabonibacter 1.24±0.78 1.74±0.87 0.95±0.58 1.04±0.64
Odoribacter 1.22±0.94 1.36±1.26 1.11±0.8 1.2±0.72
Parabacteroides 1.2±0.61 1.12±0.79 1.17±0.58 1.32±0.42
Choladousia 1.17±1.12 1.77±1.17 0.68±0.86 1.05±1.09
UBA3402 1.1±0.68 1.13±0.61 1.12±0.74 1.05±0.74
Nanosyncoccus 0.99±1.54 0.21±0.25 2.18±2.22 0.57±0.41
Akkermansia 0.97±5.81 0±0 0±0 2.9±10.06
Merdiplasma 0.96±1.7 2.87±1.81 0.01±0.01 0.01±0.01
COE1 0.95±1.32 0.75±2.06 1.17±0.92 0.93±0.55
UBA3282 0.9±1.95 0.04±0.03 0.84±1.31 1.81±2.95
Blautia_A 0.86±1.19 1.48±1.84 0.5±0.36 0.6±0.6
RUG626 0.82±1.2 0.17±0.25 1.12±1.01 1.19±1.68
V9D3004 0.82±1.18 1.1±1.85 0.7±0.62 0.66±0.71
Ventricola 0.81±0.91 0.84±0.69 0.51±0.5 1.08±1.32
Victivallis 0.79±0.65 1.03±0.58 0.84±0.82 0.5±0.42
MGBC124762 0.78±1.48 1.46±2.23 0.28±0.42 0.59±0.99
Fimenecus 0.64±0.77 0.2±0.52 1.11±0.9 0.62±0.61
Pyramidobacter 0.59±0.56 1.25±0.44 0.19±0.15 0.32±0.21
Stercorousia 0.58±1.99 0.18±0.57 0.89±3.02 0.67±1.69
CAG-485 0.56±0.61 0.23±0.48 0.56±0.62 0.9±0.56
Paraprevotella 0.55±1.81 1.42±3.02 0.16±0.2 0.08±0.05
Parasutterella 0.51±0.39 0.46±0.39 0.6±0.52 0.47±0.24
Mailhella 0.46±0.41 0.92±0.32 0.23±0.19 0.22±0.21
CALXXL01 0.44±0.39 0.38±0.28 0.54±0.52 0.41±0.34
Avilachnospira 0.44±0.99 1.31±1.36 0±0 0.02±0.04
Choladocola 0.44±0.55 0.22±0.6 0.59±0.58 0.5±0.45
Ruminococcus 0.43±0.62 0.33±0.33 0.34±0.27 0.61±0.99
CAG-590 0.42±0.94 0.04±0.09 0.75±1.28 0.47±0.94
Roseburia 0.42±0.87 0.08±0.11 0.43±0.67 0.73±1.31
UBA3855 0.41±0.57 0.29±0.65 0.34±0.39 0.6±0.64
HGM10766 0.41±2.16 0±0 1.19±3.72 0.02±0.03
Monoglobus 0.41±0.38 0.23±0.39 0.56±0.39 0.42±0.3
MGBC105563 0.4±0.77 0.12±0.24 0.63±0.89 0.43±0.94
UMGS1601 0.4±0.65 0.15±0.29 0.32±0.37 0.72±0.97
Pelethomonas 0.39±0.43 0.27±0.63 0.35±0.21 0.54±0.31
Scatovivens 0.38±0.94 0±0 0.86±1.45 0.28±0.56
Caccocola 0.37±0.41 0.61±0.52 0.26±0.38 0.25±0.17
Gemmiger 0.37±0.92 0.53±1.18 0.36±1.04 0.21±0.42
Phocaeicola_A 0.35±0.34 0.5±0.45 0.24±0.19 0.32±0.31
CAG-452 0.34±0.93 0.02±0.03 0.86±1.48 0.15±0.32
Muribaculum 0.34±1.08 0.01±0.01 0.4±1.09 0.6±1.53
UMGS1663 0.34±0.87 0.05±0.04 0.6±1.29 0.37±0.73
14-2 0.32±1.87 0±0.01 0.94±3.24 0.01±0
CAJMNU01 0.3±1.8 0±0 0.9±3.12 0.01±0
UBA1213 0.3±0.54 0.09±0.24 0.27±0.31 0.54±0.82
UBA6857 0.29±0.41 0.7±0.51 0.08±0.05 0.08±0.04
UBA1224 0.29±0.73 0.85±1.09 0.01±0.01 0.01±0.01
CALWRD01 0.29±0.32 0.06±0.09 0.39±0.34 0.41±0.35
Paramuribaculum 0.26±0.38 0.04±0.02 0.26±0.25 0.49±0.52
JAJQCX01 0.25±0.32 0.02±0.01 0.24±0.22 0.49±0.39
Marvinbryantia 0.25±0.35 0.06±0.02 0.42±0.43 0.26±0.38
SIG200 0.23±0.79 0±0 0.54±1.29 0.16±0.41
SFTH01 0.23±0.35 0.47±0.5 0.11±0.11 0.1±0.14
Aphodocola 0.22±0.79 0±0 0.61±1.3 0.06±0.18
Limadaptatus 0.21±0.49 0±0 0.38±0.7 0.26±0.41
CAG-475 0.21±0.52 0±0 0.23±0.51 0.39±0.72
Copromonas 0.21±0.22 0.24±0.32 0.13±0.12 0.25±0.18
CAG-95 0.21±0.97 0.01±0.01 0.07±0.05 0.54±1.67
Schaedlerella 0.2±0.47 0.02±0.01 0.15±0.15 0.44±0.75
Lawsonibacter 0.2±0.26 0.39±0.27 0.09±0.2 0.11±0.22
Eubacterium_R 0.19±0.34 0.03±0.01 0.32±0.39 0.24±0.4
RGIG2066 0.19±0.4 0.58±0.51 0±0 0±0
CAG-552 0.19±0.31 0.01±0.03 0.25±0.29 0.29±0.41
CAG-353 0.18±0.86 0±0 0.41±1.42 0.14±0.48
Acetatifactor 0.18±0.21 0.06±0.04 0.26±0.31 0.23±0.15
RUG115 0.18±0.47 0.34±0.8 0.12±0.08 0.09±0.06
Marseille-P3106 0.17±0.37 0.02±0.04 0.22±0.3 0.28±0.55
Alistipes_A 0.17±0.12 0.09±0.08 0.23±0.1 0.2±0.13
UMGS2069 0.17±0.18 0.12±0.14 0.21±0.27 0.17±0.11
Dysosmobacter 0.16±0.16 0.24±0.22 0.13±0.12 0.12±0.08
Duncaniella 0.16±0.93 0±0 0.01±0.02 0.48±1.61
Eubacterium_I 0.16±0.28 0.44±0.35 0.01±0.01 0.02±0.03
Eubacterium_G 0.15±0.27 0.01±0.01 0.28±0.39 0.17±0.2
Pseudoruminococcus 0.15±0.34 0±0 0.2±0.37 0.26±0.43
Ventrimonas 0.15±0.27 0.29±0.37 0.12±0.24 0.03±0.02
UMGS995 0.15±0.52 0±0 0.4±0.87 0.04±0.07
Paenalcaligenes 0.14±0.81 0±0 0.43±1.4 0±0.01
Merdimorpha 0.14±0.15 0.08±0.09 0.2±0.19 0.15±0.13
CAG-103 0.14±0.49 0±0 0.13±0.39 0.3±0.75
MGBC163016 0.14±0.15 0.05±0.04 0.23±0.19 0.15±0.13
CAG-793 0.14±0.34 0.02±0.02 0.23±0.42 0.17±0.4
RGIG3926 0.14±0.35 0.41±0.53 0±0 0±0
CAG-41 0.14±0.26 0.03±0.05 0.19±0.35 0.19±0.27
CALXFK01 0.13±0.18 0.05±0.07 0.21±0.25 0.14±0.15
Onthocola_B 0.13±0.42 0±0 0.38±0.68 0.01±0.02
Butyricicoccus_A 0.13±0.47 0.36±0.78 0.01±0.01 0.01±0.02
Coprococcus 0.13±0.33 0.01±0.02 0.11±0.09 0.27±0.55
UBA5578 0.13±0.58 0±0.02 0.08±0.19 0.29±0.99
Caccovivens 0.12±0.36 0±0 0.2±0.44 0.18±0.43
RUG11788 0.12±0.37 0±0 0.08±0.19 0.28±0.59
CAG-266 0.12±0.38 0±0 0.17±0.41 0.18±0.51
Mediterranea 0.11±0.6 0.31±1.03 0.01±0.01 0.01±0.01
Fimivicinus 0.11±0.43 0.01±0.02 0.01±0 0.32±0.71
Acutalibacter 0.11±0.15 0.08±0.05 0.11±0.16 0.13±0.19
UBA11774 0.1±0.37 0.26±0.62 0.03±0.02 0.02±0.02
Hominisplanchenecus 0.1±0.08 0.13±0.06 0.1±0.11 0.08±0.05
CALXJL01 0.1±0.54 0±0 0.3±0.93 0.01±0.01
Ventrenecus 0.1±0.38 0±0 0.28±0.63 0.01±0.01
Flavobacterium 0.1±0.57 0±0 0.28±0.98 0±0
Halomonas 0.09±0.29 0±0 0.15±0.36 0.13±0.36
UBA738 0.09±0.33 0.27±0.55 0±0 0.01±0
JAAWPK01 0.09±0.27 0.12±0.26 0.13±0.39 0.03±0.05
RGIG2774 0.09±0.3 0.25±0.49 0±0 0±0
UBA3789 0.09±0.34 0±0 0.03±0.03 0.23±0.58
UMGS973 0.08±0.17 0.22±0.24 0.01±0.02 0.02±0.03
CALWPC01 0.08±0.17 0.19±0.26 0.04±0.05 0.02±0.01
Methanosphaera 0.08±0.22 0±0 0.09±0.23 0.15±0.29
Geddesella 0.08±0.15 0.02±0.06 0.09±0.05 0.14±0.25
UMGS1124 0.08±0.44 0±0 0±0.01 0.23±0.76
Butyribacter 0.08±0.11 0.01±0.01 0.14±0.13 0.08±0.12
Limivicinus 0.08±0.21 0±0 0.07±0.16 0.15±0.31
F23-B02 0.08±0.26 0±0 0.01±0.02 0.22±0.43
Zag111 0.07±0.43 0±0 0±0.01 0.22±0.74
Avispirillum 0.07±0.11 0±0 0.13±0.15 0.08±0.07
UBA11524 0.07±0.3 0±0 0.03±0.05 0.18±0.52
Aliidiomarina 0.07±0.24 0.02±0.04 0.12±0.38 0.08±0.18
Thiopseudomonas 0.07±0.41 0±0 0.21±0.71 0±0
Faecousia 0.07±0.25 0.01±0.01 0.19±0.42 0.01±0.01
Ignatzschineria 0.07±0.4 0±0 0.2±0.68 0±0
Luteimonas_D 0.07±0.24 0±0 0.06±0.16 0.14±0.38
Cryptoclostridium 0.06±0.11 0.12±0.17 0.03±0.02 0.04±0.02
CAG-115 0.06±0.34 0±0 0.18±0.59 0±0
UBA11452 0.06±0.12 0.06±0.17 0.07±0.1 0.06±0.06
UBA1428 0.06±0.11 0.15±0.16 0.01±0.01 0.02±0.04
RUG11130 0.06±0.15 0.02±0.02 0.12±0.25 0.04±0.03
JAFLUQ01 0.06±0.18 0.17±0.29 0.01±0.01 0±0
RUG14903 0.06±0.11 0.01±0.01 0.13±0.16 0.04±0.04
CAG-273 0.06±0.24 0±0 0.15±0.41 0.01±0.02
12844 0.06±0.15 0.01±0.02 0.04±0.05 0.12±0.24
Onthoplasma 0.05±0.32 0±0 0±0 0.16±0.55
Enterocloster 0.05±0.09 0.06±0.15 0.05±0.05 0.05±0.05
RUG591 0.05±0.3 0±0 0.15±0.53 0±0
Heteroclostridium 0.05±0.06 0.01±0.01 0.07±0.06 0.08±0.06
UBA2882 0.05±0.27 0±0 0.14±0.47 0.02±0.06
JAAWJJ01 0.05±0.12 0.15±0.17 0±0 0±0
CAKSQF01 0.05±0.08 0±0 0.08±0.09 0.06±0.09
Scatocola 0.05±0.22 0.03±0.1 0.11±0.37 0±0
Eubacterium_F 0.05±0.12 0.01±0.01 0.1±0.19 0.04±0.07
Ornithomonoglobus 0.05±0.14 0.13±0.23 0.01±0.02 0±0.01
Emergencia 0.05±0.08 0.13±0.08 0±0 0±0
UBA7067 0.05±0.1 0.02±0.05 0.05±0.08 0.06±0.15
Oceanisphaera 0.05±0.27 0±0 0.14±0.46 0±0
Ruminococcus_E 0.04±0.15 0.08±0.23 0.05±0.14 0.01±0.01
RUG12438 0.04±0.18 0±0 0.03±0.07 0.1±0.3
Caccovicinus 0.04±0.21 0±0.01 0.11±0.36 0.01±0.01
Ruminococcus_C 0.04±0.08 0.03±0.1 0.05±0.05 0.04±0.09
RGIG7179 0.04±0.08 0.12±0.09 0±0 0±0
UBA7185 0.04±0.06 0.1±0.07 0.01±0.01 0.01±0.01
UBA1066 0.04±0.09 0.12±0.12 0±0 0±0
Ruminiclostridium_E 0.04±0.18 0±0 0±0 0.11±0.31
Blautia 0.04±0.19 0±0 0.01±0.01 0.1±0.33
RUG11890 0.04±0.14 0±0 0.04±0.07 0.07±0.23
RUG762 0.04±0.17 0±0.01 0.09±0.3 0.01±0
Faecalimonas 0.03±0.11 0.1±0.17 0±0 0±0
MGBC119817 0.03±0.07 0±0 0.07±0.11 0.03±0.05
Scybalousia 0.03±0.16 0±0 0.08±0.28 0.02±0.07
Alangreenwoodia 0.03±0.06 0.1±0.06 0±0 0±0
Ruthenibacterium 0.03±0.05 0±0.01 0.05±0.07 0.04±0.05
UBA3818 0.03±0.05 0±0 0.03±0.02 0.07±0.06
Jeotgalicoccus 0.03±0.16 0±0 0.09±0.27 0.01±0.02
Oligella 0.03±0.18 0±0 0.09±0.32 0±0.01
Coprovivens 0.03±0.19 0±0 0±0 0.09±0.33
UBA2664 0.03±0.14 0±0 0.02±0.08 0.07±0.23
Bilophila 0.03±0.02 0.05±0.02 0.02±0.01 0.02±0.01
MGBC100174 0.03±0.06 0.03±0.07 0.02±0.03 0.04±0.06
Avigastranaerophilus 0.03±0.17 0.09±0.29 0±0 0±0
Butyricimonas 0.03±0.07 0.01±0 0.05±0.11 0.03±0.03
Staphylococcus 0.03±0.12 0±0 0.08±0.2 0.01±0.02
Corynebacterium 0.03±0.06 0±0 0.05±0.08 0.04±0.05
RGIG3102 0.03±0.06 0.08±0.09 0±0 0±0
JABUSF01 0.03±0.07 0±0 0.04±0.06 0.04±0.1
Salinicoccus 0.03±0.06 0±0 0.02±0.04 0.06±0.08
Protoclostridium 0.03±0.15 0±0 0±0 0.08±0.26
QAKD01 0.03±0.08 0.08±0.12 0±0 0±0
UBA3305 0.02±0.11 0.06±0.18 0.01±0.01 0.01±0
RGIG1902 0.02±0.04 0.06±0.04 0±0 0±0
Metalachnospira 0.02±0.04 0.06±0.05 0±0.01 0.01±0.01
CADBMC01 0.02±0.04 0.06±0.06 0.01±0.01 0±0.01
Angelakisella 0.02±0.05 0.07±0.06 0±0 0±0
SIG32 0.02±0.04 0±0 0.02±0.02 0.04±0.06
Faecivivens 0.02±0.04 0.06±0.06 0±0 0±0
MGBC108787 0.02±0.11 0±0 0±0 0.06±0.19
Lachnoclostridium_B 0.02±0.06 0±0 0.05±0.11 0.01±0.01
TWA4 0.02±0.03 0.04±0.04 0.01±0.01 0.01±0.01
CAKOLD01 0.02±0.1 0±0 0.06±0.16 0±0
HGM12545 0.02±0.1 0±0 0.05±0.16 0.01±0.02
Firm-04 0.02±0.08 0.04±0.14 0.01±0.02 0.01±0.01
HGM13634 0.02±0.09 0±0 0.05±0.16 0.01±0.02
Marinobacter 0.02±0.08 0±0 0.04±0.13 0.02±0.03
KLE1615 0.02±0.05 0.05±0.08 0±0 0±0
MGBC113645 0.02±0.1 0±0 0.05±0.17 0±0
CAKSEI01 0.02±0.04 0±0 0.04±0.06 0.01±0.01
UMGS1994 0.02±0.05 0±0 0.04±0.09 0.01±0.01
Fusicatenibacter 0.02±0.05 0±0 0±0.01 0.05±0.07
Wohlfahrtiimonas 0.01±0.08 0±0 0.04±0.14 0±0
Onthousia 0.01±0.08 0±0 0.04±0.13 0±0
UMGS1754 0.01±0.06 0±0 0.03±0.1 0±0
Caccenecus 0.01±0.06 0±0 0.03±0.09 0.01±0.02
UBA1752 0.01±0.04 0±0 0.02±0.08 0±0.01
CAG-272 0.01±0.05 0±0 0.02±0.08 0±0
Pseudomonas 0.01±0.01 0±0.01 0±0 0.01±0.01
JABCPE02 0±0 0±0 0±0 0±0
Streptococcus 0±0 0±0 0±0 0±0
Alysiella 0±0 0±0 0±0 0±0
Neisseria 0±0 0±0 0±0 0±0
HGM08974 0±0 0±0 0±0 0±0
Pelistega 0±0 0±0 0±0 0±0
Moraxella 0±0 0±0 0±0 0±0
Actinobacillus 0±0 0±0 0±0 0±0
GN02-873 0±0 0±0 0±0 0±0
genus_arrange <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=sum(relabun)) %>%
    filter(genus != "g__")%>%
    arrange(-mean) %>%
    select(genus) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    pull()

genus_summary_sort <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=mean(relabun, na.rm=T),sd=sd(relabun, na.rm=T)) %>%
    arrange(-mean) 

genus_summary %>%
  mutate(genus=factor(genus, levels=rev(genus_summary_sort %>% pull(genus)))) %>%
  filter(relabun > 0) %>%
  ggplot(aes(x=relabun, y=genus, group=genus, color=phylum)) +
  scale_color_manual(values=phylum_colors) +
  geom_jitter(alpha=0.5) + 
  facet_grid(.~diet)+
  theme_minimal() + 
  theme(axis.text.y = element_text(size=6))+
  labs(y="Family", x="Relative abundance", color="Phylum")